scholarly journals Mineral quantification at deposit scale using drill-core hyperspectral data: a case study in the Iberian Pyrite Belt

2021 ◽  
pp. 104514
Author(s):  
Roberto De La Rosa ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Moritz Kirsch ◽  
Guillem Gisbert ◽  
...  
Keyword(s):  
Iberian Pyrite Belt ◽  
Hyperspectral Data ◽  
Drill Core

scholarly journals Geospatial Serverless Computing: Architectures, Tools and Future Directions

2020 ◽  
Vol 9 (5) ◽  
pp. 311 ◽  
Author(s):  
Sujit Bebortta ◽  
Saneev Kumar Das ◽  
Meenakshi Kandpal ◽  
Rabindra Kumar Barik ◽  
Harishchandra Dubey
Keyword(s):  
Big Data ◽  
Forest Cover ◽  
Mineral Resources ◽  
Hyperspectral Data ◽  
Real Time Analysis ◽  
First Case ◽  
Amazon Web Services ◽  
Aws Lambda ◽  
Physical Features

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

scholarly journals Economics of a variable rate fertiliser strategy on a Whanganui hill country station

2017 ◽  
Vol 79 ◽  
pp. 125-129
Author(s):  
M.D. White ◽  
A.K. Metherell ◽  
A.H.C. Roberts ◽  
R.E. Meyer ◽  
T.A. Cushnahan
Keyword(s):  
Economic Benefit ◽  
Net Present Value ◽  
Hyperspectral Data ◽  
Variable Rate ◽  
Differential Gps ◽  
Hill Country ◽  
The Face ◽  
Gross Margins ◽  
Fertiliser Strategy

Abstract Automated flow control coupled to differential GPS guidance systems in aerial topdressing aircraft will allow variable rate (VR) fertiliser strategies to be applied on hill country farms. The effectiveness of these strategies will be enhanced with the use of remotely sensed hyperspectral data to categorise and quantify the farm landscape in greater detail. The economic benefit of a variable rate fertiliser strategy in comparison to a single rate (blanket) strategy was evaluated for a case study Whanganui hill country station. The analysis illustrates the robustness of a VR strategy in the face of volatile returns in that it produced a higher 10 year cumulative net present value (NPV) and remained at a positive advantage at three different stock gross margins, in comparison to a blanket approach. The effectiveness of hyperspectral imagery for defining effective pasture areas to assist development of more precise variable rate fertiliser applications, compared to the current visual classification from farm photography is discussed. Keywords: economic benefit, variable rate fertiliser, hyperspectral data

scholarly journals BATHYMETRY MAPPING USING HYPERSPECTRAL DATA: A CASE STUDY OF YAMADA BAY, NORTHEAST JAPAN

2016 ◽  
Vol XLI-B7 ◽  
pp. 61-64
Author(s):  
E. Ariyasu ◽  
S. Kakuta ◽  
T. Takeda
Keyword(s):  
Water Depth ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Inversion Method ◽  
Main Island ◽  
Inherent Optical Properties ◽  
Ground Survey ◽  
Iwate Prefecture ◽  
Sand Bottom

This study aims to examine if the inversion method using hyperspectral data is applicable in Japan. Nowadays, overseas researchers are mainly applied an inversion method for accurately estimating water depth. It is able to gain not only water depth, but also benthic spectral reflection and inherent optical properties (IOPs) at the same time, based on physics-based radiative transfer theory for hyperspectral data. It is highly significant to understand the possibility to develop the application in future for coastal zone of main island, which is a common water quality in Japan, but there is not any case study applied this method in Japan. The study site of Yamada bay in Iwate Prefecture is located in northeast of Japan. An existed analytical model was optimized for mapping water depth in Yamada bay using airborne hyperspectral image and ground survey data which were simultaneously acquired in December, 2015. The retrieved remote-sensing reflectance (R<sub>rs</sub>) is basically qualitatively appropriate result. However, when compared with all ground survey points, the retrieved water depth showed low correlation, even though ground points which are selected sand bottom indicates high relationship. Overall, we could understand the inversion method is applicable in Japan. However, it needs to challenge to improve solving error-caused problems.

A supervised technique for drill-core mineral mapping using Hyperspectral data

2020 ◽  
Author(s):  
Cecilia Contreras ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Core Logging ◽  
Learning Techniques

&lt;p&gt;Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores&lt;br&gt;are first logged in the field by a geologist and with regards to, e.g., mineral assemblages,&lt;br&gt;alteration patterns, and structural features. The core-logging information is then used to&lt;br&gt;locate and target the important ore accumulations and select representative samples that are&lt;br&gt;further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray&lt;br&gt;diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and&lt;br&gt;subject to the expertise of the geologist.&lt;br&gt;Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly&lt;br&gt;being used to support the geologist in the analysis of drill-core samples. Nonetheless, the&lt;br&gt;benefit and impact of using hyperspectral data depend on the applied methods. With this in&lt;br&gt;mind, machine learning techniques, which have been applied in different research fields,&lt;br&gt;provide useful tools for an advance and more automatic analysis of the data. Lately, machine&lt;br&gt;learning frameworks are also being implemented for mapping minerals in drill-core&lt;br&gt;hyperspectral data.&lt;br&gt;In this context, this work follows an approach to map minerals on drill-core hyperspectral data&lt;br&gt;using supervised machine learning techniques, in which SEM data, integrated with the mineral&lt;br&gt;liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution&lt;br&gt;mineralogical data obtained by SEM-MLA analysis is resampled and co-registered&lt;br&gt;to the hyperspectral data to generate a training set. Due to the large difference in spatial&lt;br&gt;resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is&lt;br&gt;required to link these two images at the hyperspectral data spatial resolution. In this study,&lt;br&gt;we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral&lt;br&gt;pixel in the corresponding SEM-MLA region. We then use the abundances as features in a&lt;br&gt;clustering procedure to generate the training labels. In the final step, the generated training&lt;br&gt;set is fed into a supervised classification technique for the mineral mapping over a large area&lt;br&gt;of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave&lt;br&gt;infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral&lt;br&gt;mapping task improves significantly.&lt;/p&gt;

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